Retail stores are in business to sell merchandise and make a profit. Store managers are most concerned with product-related marketing and decisions such as product placement, assortment, space, price, promotion, and inventory. If the products are non-optimized in terms of these product decisions, then sales can be lost and profit will be less than what would otherwise be possible in an optimal system. For example, if the product assortment, space, or inventory is not properly selected or maintained, then the consumer is less likely to buy these products. If price is too high or too low, then profit can be lost. If promotions are not properly targeted, then marketing efforts will be wasted. If the product placement is poorly laid-out, then the store loses sales.
In a similar manner, suppliers (manufacturers and distributors) who supply to retail stores are in business to sell merchandise and make a profit. Suppliers are concerned with manufacturing, inventory, price, promotions, transportation, delivery schedules, returns, and seasonal merchandise. Yet, both retailers and suppliers share common concerns as they are inherently connected by supply chain logistics and economics. Problems at one end of the supply chain can adversely affect the profitability of another part of the supply chain. For example, if the supplier has not properly planned for inventory requirements for a promotional item, then the product may not be available to meet the retail demand or the supplier may be left with excess inventory at the end of the promotion. If the products sold by the suppliers are non-optimized in terms of their product decisions, then sales may be lost and profit will be less than what would otherwise be possible in an optimal system.
In order to maximize the outcome of product-related decisions, retail store and supplier management have used statistical modeling and strategic planning to optimize the decision making process for many product decisions. Economic modeling and planning is commonly used to estimate or predict the performance and outcome of real systems, given specific sets of input data of interest. A model is a mathematical expression or representation which predicts the outcome or behavior of the system under a variety of conditions. An economic-based system will have many variables and influences which determine its behavior. In one sense, it is relatively easy to review historical data, understand its past performance, and state with relative certainty that the system's past behavior was indeed driven by the historical data. A much more difficult task, but one that is extremely important and valuable, is to generate a mathematical model of the system, which predicts how the system will behave, or would have behaved, with different sets of data and assumptions. The field of probability and statistics has provided many tools, which allow predictions to be made with reasonable certainty and acceptable levels of confidence.
In its basic form, the economic model can be viewed as a predicted or anticipated outcome of a mathematical expression, as driven by a given set of input data and assumptions. The input data is processed through the mathematical expression representing either the expected or current behavior of the real system. The mathematical expression is formulated or derived from principles of probability and statistics, often by analyzing historical data and corresponding known outcomes, to achieve an accurate correlation of the expected behavior of the system to other sets of data. In other words, the model should be able to predict the outcome or response of the system to a specific set of data being considered or proposed, within a level of confidence, or an acceptable level of uncertainty.
Economic modeling has many uses and applications. One emerging area in which modeling has exceptional promise is in the retail sales and supplier environments. Grocery stores, general merchandise stores, specialty shops, and other retail outlets face stiff competition for limited customers and business. Suppliers must manage the supply chain to service the retailers. Retailers and suppliers alike make every effort to maximize sales, volume, revenue, and profit. Economic modeling can be an effective tool in helping retail storeowners and suppliers achieve these important goals.
Retailers and suppliers have traditionally used a variety of modeling tools to represent and optimize one or more of the product decisions described above, i.e., product placement, assortment, space, price, promotion, inventory, delivery, and seasonal merchandise. One modeling tool may optimize for product placement, assortment, and space. Another modeling tool will optimize inventory. Yet another modeling tool may optimize for price. Still another modeling tool will predict optimal manufacturing schedules. Each modeling tool may yield good results for the specific criteria being considered.
Retailers and suppliers each generate and track different data and utilize different models customized to their business for planning purposes. The cost of creating and executing the different models reduces the overall profitability for both parties. Moreover, getting suppliers and retailers to share information is no small feat. Suppliers and retailers are cautious to the idea of sharing data because of competitive concerns. The data may be only partially disclosed or tightly restricted as to its dissemination and use. Even when shared, the data is not necessarily compatible with the recipient's model. The recipient may not be able to make any meaningful judgments from the limited shared data. In addition, the output of these models do not necessarily correlate, which inhibits the supply chain members from reaching any mutually beneficial or optimal supply chain management and product decisions. For example, the supplier may, with its data and models, forecast a given demand for a product based on a given price and promotion. The retailer could run the same product pricing and promotion and get a different demand forecast based on its data and models. With the present supply chain system that tends toward isolation of data and individualized modeling tools, suppliers and retailers are often disjointed in making important product decisions.
A need exists to improve the performance of supply chain logistics and economics for both suppliers and retailers.